spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.


Reviewer #1
This manuscript very nicely describes a technique to enrich for high quality RNA, while still maintaining high-throughput single cell analysis, no easy task.

The characterization of their system is thorough and expansive, testing live cells and the very difficult PFA-fixed cells. The sorting for viability after coencapsulation is very clever and I have no doubt it will contribute greatly to single cell analysis field and attract attention from companies such as 10X genomics. The reagent addition via dielectrophoretic picoinjection is also very interesting with an even broader range of applicability of interest from others in the microfluidics field. All around, I think the manuscript is wonderful, I just have several comments below mainly out of interest generated as I read the manuscript (which has data that is well analyzed and is also well written).
We thank the reviewer for their input and share their enthusiasm about the spinDrop methodology. We especially thank the reviewer for the useful instructive comments that we have addressed below and in the manuscript (shown in red). Unfortunately, the version of the algorithm we used does not save the samples of raw recordings, but the example of arbitrary threshold using both amplitude and the droplet residence time (width of the peak) is presented in the extended data fig. 2 and we show a rendering of a typical recording, digitised from a snapshot of the sorting interface in Figure R1.1a. The baseline frequency of the signal represents each travelling droplet. The droplet containing a green fluorescent cell will pass the sorting R1 threshold based on amplitude (dotted line), and, if the area of the signal aligns with other singlets, the droplet is sorted (similarly to FACS). The variability of fluorescence signal results from the heterogeneity of the cell size because of differences in cellcycle stage, mainly; or more broadly intracellular esterase content, although this is arguably hard to quantify. However, the thresholding on fluorescence is kept broad and is solely applied to remove the background empty droplets or droplets with damaged cells, or very bright cellular aggregates, which can clearly be distinguished and are presented in Figure 2A. Although our analysis reveals the proportion of cells per cell-type is conserved in the mouse brain dataset for the sample with and without sorting ( Figure R1.1b,c); and that cell-cycle phases throughout the mouse brain and B-cell sorting datasets are broadly similar with and without sorting ( Figure R1.d,e), which indicates that our sorter performs indiscriminately from cell-size and fluorescence levels, a possible iteration on the current technology would be to implement multi-modal sorting e.g. combining the fluorescence readout with imagebased analysis to integrate this information in the subsequent single-cell analysis and increase confidence in the populations sorted. We have added these points of discussion in the main text and discussion section: 1) "To verify that our sorting parameters did not significantly affect the population of B-cells profiled (mainly due to their size), cell-cycle phase was profiled for all datasets, revealing that the proportion of cells in each phase was broadly similar between all datasets. This observation refutes that selections were based on cell sizes as the latter varies significantly throughout cell-cycle stages (Extended Data Figure 2F)." 2) "Sorting using FADS did not affect cell-type representation (apart from a slight overrepresentation of fibroblasts in the FADS dataset) or the proportion of cellcycle phase, showing that the method is broadly applicable to cell atlasing and does not affect cell-type representation (Extended Data Figure 4E In our hands, the sorting process performs robustly, as shown in the results presented in Figure 2C, demonstrating that only a few live cells remain unsorted ( Figure 2C). During the experiments, we have not noticed droplets that are not pulled despite being detected. The refractory time of the electronics is very short and does not influence the sorting process -a field-programmable gate array (FPGA) used in our system has a sampling rate of 200 kHz (which means that sorting operations can be in theory executed every 5 microseconds). Practically the throughput is limited by the duration of the high-voltage pulse that usually lasted 1 ms.
We have run an image analysis of the videos with droplets and calculated the relative standard deviation (RSD) of the droplet area (in the 2D image) to be equal to 4.2%, which can be translated to the RSD of droplet volume to be around 6.3% (assuming a spherical droplet shape). We have amended the droplet volume in the text to 1.3 ± 0.1 nL. This size variation of droplets generated in spIndrop is slightly larger than for droplets typically formed by microfluidic devices (standard microfluidic flow-focusing junction generates emulsions with polydispersity index typically < 1% 1 ). This is due to the imbalance in droplet generation because of the barcoded bead packing process, which negatively affected droplet monodispersity compared to purely aqueous droplets (a general limitation of single-cell technologies). In our experience, it is not the variation in droplet sizes that affects the throughput of the R4 sorting, but rather the uneven spacing between the droplets due to uneven bead packing. Reduced solid-support bead sizes would reduce this imbalance and in turn ameliorate throughput. We have added this point of discussion in the manuscript with the following text: "Sorting throughputs might be increased by using smaller beads that would provide better droplet monodispersity by reducing the droplet volume, or using serial electrodes that can improve sorting speed 2 ." Also, after looking at Supplementary Video 1, it seems like the applied dielectrophoretic force cause droplet deformation, does this ever result in droplet breaking? What is the optimization done here to provide a strong enough force for sorting, but not strong enough to break the droplet?
We have not observed any droplet breaking in 3 recorded video traces across 60 droplets, and in our hands and according to our sorting output calculations, the sorter performs robustly throughout multiple experiments. However, we agree that sorting of droplets for single-cell genomic assays is more challenging due to the presence of high concentrations of water-soluble surfactants (e.g. Igepal CA-630) in the lysis mix. Under these conditions, the interfacial tension between the droplet aqueous phase and the oil fluorocarbon phase is much lower than normal, which makes the droplet more "deformable" and more difficult to transport to the sorting channel using an electric field. To alleviate this, we made the sorting junction deeper than the droplet generation junction (which required the fabrication of a 3-layer chip).
As a result, droplets are spherical at the sorting junction, rather than squeezed, and they are much easier to translocate with an electric pulse. To clarify these optimisations, we have added the following sentence in the methods section: "Additionally, we implemented a gapped divider at the sorting junction 3 that gradually pushes droplets to the outlet channels and minimizes the risk of droplet breaking." We have also reviewed our description of the sorting protocol and added a sentence emphasising the importance of adjusting flow resistance in the negative channel (unsorted droplets).

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In extended data Figure B, you are missing the label "4" We thank the reviewer for this comment. We assumed the reviewer asked for Extended Data Figure 1B, which has a label 4, however it may have been harder to spot as it is located at the bottom of the chip as opposed to the other labels that are at the top ( Figure R1.2).  In your supplementary Video 2, there is a "miss" where the picoinjection droplet misses the droplet and forms a smaller droplet, not containing a cell.
How often does this event occur? What do you do with these miss-injected droplets? What and how is the timing if picoinjection controlled? There also seems to be a variety of droplet sizes in this video, can you remark on this?
We thank the reviewer for pointing this out. The higher variation in droplet volumes (as discussed in one of the previous questions) resulted in less even spacing of droplets during droplet generation. To better explain this phenomenon, we have added the following text in the methods section of the manuscript: "The variation in droplet sizes caused uneven spacing during droplet reinjection which occasionally resulted in the formation of smaller 'orphan' droplets with RT buffer (as visible in R7 Supplementary Video 2). Formation of these buffer droplets was rare and did not impact the reaction inside the droplets with single-cell lysates. Overall, 98% of droplets were picoinjected correctly using this picoinjection geometry." 5% 008-FluoroSurfactant seems like a lot (line 1003), was this optimized? How did you arrive at this concentration and surfactant?
During our experiments, we observed that the 2% fluorosurfactant concentration used routinely for droplet assays does not fully prevent droplet merging during the lysis step at 70 °C. We therefore used 5% surfactant in our assay following the instructions found in another protocol -the MaPS-seq assay, a modification of inDrop method for spatial metagenomics of gut microbiome, developed by Sheth et al. 4 . This protocol routinely used 5% RAN fluorosurfactant for co-encapsulation of cells and beads and higher concentrations (30%) were used for subsequent emulsion PCR. Because this optimisation worked well in our hands, we decided to use 5% fluorosurfactant throughout our assay. We have added this reference and explanation in the following text added to the methods section: "We used higher surfactant concentrations for droplet generation and picoinjection (5% RAN fluorosurfactant) as described previously in protocols requiring high-temperature incubation 4,5 ".

Line 1015, how long at 42OC?
We apologise for omitting the incubation time, which was 2 hours, we have modified the manuscript accordingly and also improved the methods section by adding further details. We thank the reviewer for their comment. We artificially killed the population of HEK293T cells with the non-ionic detergent IGEPAL-CA630 and performed dual staining with Calcein-AM (green fluorescence) and ethidium homodimer-1 (red fluorescence), which confirmed that no cells were alive after treatment (no green R8 fluorescence from Calcein-AM was detected on the haemocytometer). The "dead" cells were counted using the ethidium homodimer-1 signal and mixed 1:1 with a viable sample (>99% viable, as confirmed by the Calcein-AM stain). The cells were then sorted based on viability and resulting droplets were assessed for encapsulated cell viability on the haemocytometer. Unfortunately, because only green fluorescence is measured during the sort, it is not possible for us to provide metrics for this experiment at the sort level as only viable cells can be counted (and dead cells will be indiscriminate from empty droplets). Therefore we counted the cells contained in the droplets post-sorting using a fluorescence microscope instead of providing sort metrics. We have clarified the text to underline that the results of the sort were assessed using fluorescence microscopy to determine viability of encapsulated cells after the sort: "To further quantify the potential of our system to extract single viable cells from a challenging sample containing a large proportion of damaged cells, the input population was modified to incorporate a 1:1 ratio of dead and alive HEK293T cells treated with a dual green/red-live/dead stain (Calcein-AM and ethidium homodimer-1, respectively). To induce cell death, a concentration of 0.25% (w/v) IGEPAL CA-630 was added to half of the HEK293T and incubated on ice for 15 minutes. Sorting of a 1:1 mixed population of dead and living cells showed a marked 19-fold enrichment for viable cells from the pool of droplets containing cells, assessed for viability using a fluorescence microscope, with 84.8% of the droplets containing a single viable cell, which surpasses the predicted value of 4.52% without sorting ( Figure 2C)." Step 11, line 911, could you remark on integrity of RNA after UV exposure?
We thank the reviewer for noting this potential bottleneck, which is also found in the inDrop protocol. Although RNA is known to crosslink or degrade (wavelengths <300 nm) with UV exposure, the wavelength used (365 nm) as well as the exposure time and dose (starting the lamp switched off and 7 minutes exposure time) should not significantly affect the quality of the sample 6 , as demonstrated by the high capture rates in this study. However, it is likely that RNA-RNA or RNA-protein cross links may still hinder RNA reverse transcription 7 , however, the reverse crosslinking procedure (proteinase K digestion and heat denaturation at 70°C for 10 minutes) used in R9 spinDrop should further assist reverse-crosslinking, which may contribute to the gain in gene detection rates observed compared to the native inDrop protocol which also uses UV exposure to solubilize the barcodes in the emulsions. We have modified the manuscript accordingly with the following point of discussion, to describe other methodologies for releasing the barcode using dissolvable beads or the USER enzyme blend: "Further implementations may also include the use of dissolvable hydrogels 8 or enzymatically released barcodes 9 from the solid-support microgels to circumvent potential limitations of the current system, which uses UV-induced barcode release which may reduce RNA capture due to cross-linking.".

L i n e 2 0 6 , a n d F i g u r e 2 C , h o w i s p r e d i c t e d v a l u e c a l c u l a t e d ?
The predicted value of 4.5% for droplets containing a single viable cell was calculated as follows: first, we calculated the fraction of droplets containing a single viable cell with or without dead cells using a λ value = 0.05 for live cells, which amounted to 4.8%. Then we used the same lambda value for dead cells to calculate that 95.1% out of these 48% droplets would not contain dead cells. By multiplying 4.8% by 95.1%, we obtained 4.5% of droplets containing only a single viable cell (

2022, Neuron)
We agree that the sentence merits clarification. Background in this context relates to barcodes with low coverage that are discarded by the analysis pipeline.
Ambient RNA is indiscriminately captured in sorted and unsorted droplets, however, as the capture rates for those are likely to be similar between all droplets (with and without a cell), they would not impact barcode thresholding and selection significantly. The background barcode coverage in the text's context can result from a combination of multiple factors, such as primer dimers, concatemers, low-quality cells (or more commonly cell debris), and indeed ambient RNA. We have clarified the related figure caption to describe the nature of background as follows: "Percentage of reads that are mapped to the background, consisting mainly of primer concatemers, ambient RNA and degraded cells or cell debris; and cell barcodes for

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inDrop and inDrop with sorting, determined using the filtering statistics from the zUMIs pipeline".
We have also added a point of discussion in the text regarding ambient RNA captured in the sorted droplets, which is still an artefact present in our current implementation. Methods like SoupX 10 may still be required to remove the uniform ambient RNA signature from the resulting gene expression matrices obtained from sorted droplets. In future implementations, one could possibly reduce the droplet size used for encapsulation significantly to alleviate this problem. Indeed high cell to droplet volume ratios will ensure most of the signal comes from the cell, which leaves less space for ambient RNA to contribute to the single-cell signal i the sorted population: "However, the approach does not remove ambient RNA from sorted droplets, hence methods like SoupX may still be required to remove ambient RNA coencapsulated with cells from the dataset. Reduction of droplet size in the future (i.e. high cell volume to droplet volume ratio) may provide a viable route to remove or dampen such artefacts in future implementations." We thank the reviewer for noting the efficient RNA capture for PFA-fixed cells, we have further described these gains in a sentence: "The sample with fixed cells displayed slightly lower gene detection rates, with 1,934 genes with reads mapping to introns and 2,404 genes with reads mapping to exons ( Figure 3D). High capture rates for both cytoplasmic and nuclear RNA molecules from fixed samples will broadly expand the number of single-cell methods directly applicable in a high-throughput format, and permit single-cell sequencing after storage, which will be beneficial to clinical samples. In contrast to the newly released We have also observed mitochondrial markers in differential expression experiments between spinDrop and 10x datasets. The percentage of mitochondrial read capture is platform and protocol dependent, corroborated for example by recent cross-platform benchmarking efforts where methods that use in vitro transcriptionbased amplification rather than PCR-based, showed higher detection rates of mitochondrial reads.
In addition, it is likely that the proteinase K digestion and heat denaturation may have contributed to enhanced mitochondrial RNA-content release. Because the 10x Chromium method does not employ such harsh lysis conditions, it is likely that our improved lysis protocol may have contributed to higher mitochondrial RNA capture. This is the likeliest explanation, as the nucleus also released a higher proportion of RNA with this lysis method, as corroborated by the enhanced intronic coverage depicted in Extended Data Figure 3A. However, because the dropletQC metrics show much higher viability in the sorted sample (compared to the unsorted sample), we are confident these observations are linked to protocol-specific capture rather than to lower cell integrity.
This nuclear fraction metric is not based on mitochondrial reads as they can be celltype, species and protocol dependent, but rather on the fraction on unspliced versus spliced reads which help build a "nuclear fraction" ratio that determines alive cells from damaged cells and empty droplets. This result is corroborated by the HEK293T benchmarking effort, where viability was high. In these experiments, the median percentages of UMIs mapping to mitochondrial genes were 10.6% for 10x Chromium v2, 9.3% for inDrop and 14.0% for spinDrop ( Figure R1.5). Of note, increased mitochondrial RNA coverage is not necessarily detrimental, as demonstrated by recent advances using mutational signatures detected in mitochondrial RNA to infer cell lineage 12 . Increased coverage may thus power these variant calling analysis, which rely on the intrinsic high mutational rates of the mitochondrial genome to infer cell relationships. We have included this new analysis in Extended Data Figure 3B and added a mention in the following text: "Higher median percentage of UMIs mapping to mitochondrial genes was obtained using spinDrop (10.6% for 10x Chromium v2, 9.3% for inDrop and 14.0% for spinDrop), further underlining protocol-specific capture (Extended Data Figure 3B)." We have also added lineage tracing as a point of discussion: "Similarly, increased mitochondrial transcriptome coverage in spinDrop may benefit lineage tracing endeavours 12 . "

What is your reverse cross-linking protocol?
For reverse crosslinking, we incubate the droplets at room temperature for 30 minutes to enable proteinase K digestion followed by an incubation at 70°C for 10 minutes, we have added this information to the text and figure captions.

For Figure 5, is there a way to present a UMAP/reduced dimension with just nascent versus non-nascent then the combination of the two? This would be to better understand how the nascent transcripts dictate the broader relative situation of cells relative to one another.
We agree that this would be an interesting analysis. However, integrating these three datasets did not yield significant observable differences ( Figure R1.6).
We speculate that the subtle differences in capture between new and old transcripts in such a short time frame do not override core differences in cell types guiding dimensional reduction, meaning the old/new/old+new will look relatively similar when projecting on a dimensional reduction space. We therefore turned to MOFA (Multi-Omics Factor Analysis) 13  We agree that additional validation of the markers could strengthen the manuscript. We have therefore incorporated a novel gene ontology analysis in the R 1 9 main text to describe the overexpression of core neural markers in the spinDrop in the main text as follows: "Out of the top 100 markers overexpressed in the neuroblasts profiled using spinDrop, 43 were part of the "Out of the top 100 markers overexpressed in the radial glia profiled using spinDrop, 43 were part of the "Neural system development" gene ontology term (GO:0007399, FDR=3.1e -15 ), underlining core cell-type specific mechanisms that were absent in the equivalent 10x Chromium dataset." We thank the reviewer for their comments and pointing towards the significance of our work, we have aimed to address their concerns by providing more analysis material and description of the work in the manuscript. We have amended the manuscript according to this review using red lettering.

The authors use the FADS approach to remove doublets by applying an upper
threshold on fluorescence. One potential concern is that this may introduce bias to the profiled cell population. This should be validated. In addition, it is possible that cells in the S/G2/M phase could be filtered out in this way.
We agree with the reviewer that sorting to discard doublets may affect the atlas' content when samples contain different cell types, leading to a large range of fluorescence signal values through staining. In addition, it may be that cells in G2M may be depleted as they are larger in size than cells in G1 15 . We have therefore looked at the two datasets in the manuscript that contrast results obtained with and without droplet sorting. First, the mouse brain at E10.5 dataset was inspected. The "Bad cells" low complexity cluster was discarded from both samples (with and without FADS) as it is prominent in the "no FADS" sample and would skew proportional representation.
Looking at the global UMAP between both samples and at the cell proportions ( Figure   R2.1a,b), it appears that cell types are mostly conserved, apart from Cluster 1, which corresponds to fibroblast cells which seemed to be depleted in the sample without sorting. As fibroblasts are relatively average to large in size 16 , this finding seems to go against the possibility of excluding larger cells via upper thresholding during the R21 sorting. We note that, because the "no FADS" sample was processed after the "FADS" sample, it may be that some specific cell-type proportional representations may be skewed due to differing physical properties (i.e. sticking to the loading vessel or different sedimentation rates; or higher/lower cell death rates). In terms of cellcycle analysis, the FADS-sorted B-cells sample was depleted in the smaller G1 cells compared to the larger G2M and S cells compared to the native inDrop and 10x datasets. This would indicate again that the upper thresholding seems to not be discarding larger cells from the dataset ( Figure R2.1c). We however note that these proportions may be highly protocol and sample specific, as indicated by the cell cycle analysis offered on the mouse brain data ( Figure R2.1d). Taken

For figure 2E, it would be good for the authors to compare the transcripts detected per cell for the immune cells profiled by SpinDrop-seq and 10x.
We have computed the differential expression between the sorted B-cells for spinDrop and 10x, which are shown in Figure R2.2. However, this experiment was introduced to demonstrate the sorter (which is encompassed as a theme for Figure   2). Therefore, the improved capture is only introduced starting from Figure 3, and the transcripts captured in the PBMCs would not deviate much from the transcripts obtained with the native inDrop conditions. We initially referred to these experiments as sinDrop (sorting inDrop), but removed this annotation as it may have burdened the reader with too many different technique names.  3. What is the throughput limit of this technique? One potential concern is that the sorting process could be the speed limiting factor.
We understand this concern, however, we do not see We thank the reviewer for their comment. One of the main benefits of spinDrop is that it collapses both sorting and single-cell encapsulation into a single experimental step. Therefore, we argue in the manuscript that this can be valuable for: 1) samples with low-input, where the combination of both sorting and single-cell encapsulation may prove too lossy in cell numbers, 2) samples with low viability, which may be suitable for sorting, but may still lyse in following processing step before encapsulation. 3) samples where known transcriptional signatures emerge through lengthy R25 dissociation and handling procedures, may be susceptible to significantly altered transcriptomes by the time encapsulation takes place. Our hope is that, through spinDrop, much of the experimental variability, which is instrument, personnel and sample dependent, can be removed, by sorting droplets directly at the moment of encapsulation; and would therefore supersede the combination of FACS and encapsulation.
Another advantage is the ability to threshold on signals with large areas and amplitudes (doublets) which enable superloading of the droplets (i.e. more than one cell every ten droplets). This largely supersedes current FACS and encapsulation methods in terms of handling throughputs and will be tremendously beneficial to cell atlasing efforts. In addition, even if the cell population loaded into encapsulation maintains high viability, empty droplets are still present in traditional workflows and contribute to background noise (via primer-dimer/concatemerization for example).
In addition, although the instrument cost for spinDrop (Supplementary table   10  We agree with the reviewer that offering a comparison with the latest 10x Chromium v3 kit would have been more beneficial, but at the time of analysis, we did not have access to a HEK293T dataset using this kit. However, from the 10x application note, it appears the median genes detected for HEK293T cells with the We agree that the whole-cell mouse ES and human HEK293T cells seem to indicate slightly lower purity for the mouse cells. After careful consideration, this might be explained by the following: 1) The human HEK293T cells were of lower viability when processing or dissociating the sample, leading to higher human reads in the droplets containing mouse ES cells. We believe this might be a reasonable explanation as the comparatively similar nuclei datasets, which rely less on viability during encapsulation, appear purer.
2) Higher capture in the spinDrop protocol leads to higher multi-mapping rates, as demonstrated by Ding et al. 21 (and can be observed across methods in Figure R2.4a). The extent of cross species mapping may be dependent on protocol (as seen in R2.4c using the more sensitive 10xv3) and cell type.
We have computed the counts for species mixing experiments for the 10x v2 ( Figure   R2.4b) and v3 ( Figure R2.4c) datasets using fresh frozen human HEK293T and mouse NIH3T3 cells. From this analysis, it appears that higher capture protocols (10x v3) may indeed lead to higher cross species mapping rates. For example, it seems the barcode counts relating to mouse (0-25% fraction) is less defined for the 10x v3 protocol, compared to 10x v2. This rejoins the results obtained with spinDrop, when applying similar lower thresholds for barcode selection (n>1,000 genes per cell for either mouse or human genome; Figure R2.4d). R28 We have added a point of discussion in the text, explaining that our method does not perform well to remove ambient RNA (which would be the case in scenario 1) from sorted droplet, and a solution would be to lower the initial droplet volume: "However, the approach does not remove ambient RNA from sorted droplets, hence methods like SoupX may still be required to remove ambient RNA coencapsulated with cells from the dataset. Reduction of droplet size in the future (i.e. high cell volume to droplet volume ratio) may provide a viable route to remove or dampen such artefacts in future implementations." We agree with the reviewer that neural crest cells appear depleted compared to the sciRNA-seq and 10x datasets, there are multiple non-mutually exclusive explanations to this.
First, we note that the samples used as an input may slightly differ depending on the dissection. For example, the sciRNA-seq3 dataset is a whole-embryo sample, as neural crest cells at E11.5 are found throughout the entire organism [REF], it may be that naturally this sample is more naturally enriched in neural crest cells. For the 10x Chromium dataset, the dissection mentions: "we isolated the entire cephalic part, including prospective forebrain, midbrain and hindbrain", which resembles our dissection protocol. Therefore, it may be that capture of these cell types could be protocol-dependent. Indeed, benchmarking across sample types and single-cell methods have identified strong protocol-dependent biases in cell-type distributions 22 (Figure R2.5a) In addition, it may be that the neural crest themselves are more sensitive and were depleted in the input samples due to high cell death rates. Depletion of neural crest cells in scRNA-seq data from Neuroblastoma cells was also observed by Slyper et al. 23 , however they were present in an equivalent nuclei extracted dataset, which R31 may signify these cell types are more prone to handling damage, and may explain their "disappearance" in our low viability sample (36.6% viability) ( Figure R2.5d).

Of note, some neural crest cells are still observed in Cluster 8 as denoted by
Sox10 expression in Figure R2.5b-c. We further investigated the proportion of neural crest cells in our nascent 5EU-seq dataset. For this we took the non-nascent RNA fraction as cell-type proportions are not skewed by the analog's diffusion and tissuespecific penetrance capabilities. After label-transfer, the proportion of neural crest cells in the sciRNA-seq3 dataset was 3.8% and 2.9% in spinDrop, which shows a slight depletion in our protocol.
In Figure R2.5d, we have plotted the distribution of the number of features for each dataset, demonstrating the superior performance of 10x compared to spinDrop dataset. spinDrop however outperformed sciRNA-seq3 in terms of captured genes per cell. However, it is worth mentioning that the datasets were not downsampled by matched sequencing coverage (unlike the benchmarking in Figure 2) as we used the datasets for data integration and label transfer mainly. In addition, the spinDrop datasets were not cut-off by a lower threshold for number of genes detected, which is not the case for the 10x dataset ("Cells with fewer than 2,000 UMIs were excluded from pooling" 24 ) or the sciRNA-seq3 dataset ("Cells with fewer than 200 UMIs or over 3,172 UMIs (two standard deviations above the mean UMI count) were discarded" 25 ).
We have included these explanations in the discussion section, with the following text: "In our mouse brain analysis and nascent RNA analyses, neural crest cells were slightly depleted using spinDrop (2.9%) compared to sciRNA-seq3 (3.8%). Although neural crests have been shown to be enriched in nuclei data compared to whole-cell using a neuroblastoma clinical sample 23

How does the proportion of EU reads change across different cell types?
This should be quantified.
We agree with the reviewer that exploring nascent RNA content per cell types may shed light on transcriptional dynamics in specific tissues. In the manuscript, we and the heart fields indicating higher upticks in transcriptional activity. Of note, the allantois was removed from this analysis similarly to the data presented in Extended Data Figure 5D; there was an overlap from this cluster with low-complexity barcodes.
Therefore we preferred to exclude this sample from the proportional representation analysis. We have added these elements of information in the text: forebrain/midbrain and hindbrain were depleted (Extended Data Figure 5D). The fraction of nascent RNA reads was also smaller in tissues of neural origin, and higher in cell-types from mesodermal origin, in particular the somatic mesoderm, which displayed a high fraction of nascent RNA reads (24%) underlying tissue-specific transcriptional dynamics (Extended Data Figure 5D)."

It would be important that the authors comment on any potential limitations or concerns of the technique. Also, the author should provide detailed step-bystep protocol and computation script for the broad application of this technique.
We agree that further explanations on the potential drawbacks of the technique should be further provided, and have therefore added several new items in the discussion section as follows: 1) "The number of cells analyzed in this study (9,599)  the solid-support microgels to circumvent potential limitations of the current system, which uses UV-induced barcode release which may reduce RNA capture due to cross-linking." We have also added an experimental paragraph with detailed instructions that will enable the reader to replicate our protocol in the Methods section ("Detailed protocol for the assembly the microfluidic rig and the spinDrop chip"), including the scripts used for analysis and for sorting the droplets in LabVIEW: https://github.com/dropletlab/spinDrop (to be deposited on GitHub). We also consolidated aspects of the workflow that were mentioned previously in separate sections into one narrative.

spinDrop is proposed as an advancement for low cost, open source scRNAseq with results comparable to that of 10X Genomics. In this technique, droplet sorting allows selection of those droplets that contain cells of interest, removing a significant amount of background noise from empty cells, dead cells, and other issues. This is followed by picoinjection to introduce reverse transcription (RT) reagents to select cells, allowing harsher lysis methods while still achieving RT in-droplet, similar to inDrop and 10x genomics protocols. Overall, the manuscript is straightforward. The authors present their technological additions, test them separately to confirm that they are achieving their goals (cell sorting/RT picoinjection), optimize parameters, and compare to
other platforms.

I do not recommend this manuscript for publication as the work lacks novelty and does not seem to be of broad applicability.
We thank the reviewer for their feedback and note the main concern regarding novelty. Although it is true that several microfluidic architectures pertaining to cell and bead co-encapsulation, fluorescence-activated droplet sorting and picoinjection have been published, we believe our study present many new and unique aspects that should have a significant impact on the field of single-cell genomics; mainly performing bias-free single-cell analyses without inexact and costly computational data filtering, which has not been demonstrated previously. We have articulated our thoughts in the following bullet point list: • spinDrop is the first in-house droplet microfluidic sorting set-up with significantly improved capture efficiency for the profiling of 3' mRNA. This is crucial to derive statistical power from single-cell experiments (for differential expression analysis for example). This alone promises to deliver single-cell RNA-sequencing analysis with comparable performance to state-of-the-art commercial methods, at a fraction of the cost (0.4 USD per cell for spinDrop, 1 USD per cell for the 10x Chromium).

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• spinDrop is the first set-up to offer an elegant solution towards removing common biases found in droplet microfluidic such as empty droplets, dead cells and cell doublets. This considerably increases the accuracy of downstream analysis by removing the need for filtering the dataset with tools that show limited performance, but also significantly reduces sequencing cost by discarding the sources of bias before sequencing. We believe this is broadly transferable to other single-cell methodologies (such as ATAC-seq workflows, described using the Hydrop method 8 for example). with demonstrated high quality will prove an invaluable asset in the clinic and for fundamental research.
• spinDrop processes PFA-fixed cells without the use of targeted RNA probes, in contrast to the 10x Genomics fixed RNA profiling kit. Therefore, our workflow enables applications where coding-sequences are important, such as expression quantitative trait loci characterizations using single-cell data 26 .
Or, for example, the characterization of species that are not mouse or human, which are the two only species characterizable using the single-cell fixed RNA sequencing kit from 10x.
• spinDrop is the first method demonstrating the capabilities to support synthetic • spinDrop is the first method to show nascent RNA-sequencing using the 5EUseq method in droplets, drastically increasing the resolution of differentiation trajectories.
• spinDrop is the first method to apply 5EU-seq to a whole-organism (nascent RNA-sequencing on E8.5 mouse embryos incubated with the 5EU analog.
• spinDrop is the first method to investigate differentiation kinetics using nascent RNA sequencing and contrast with velocity at a whole-organism scale • spinDrop is the first method that employs sorting as a way to discard cellular multiplets to enable cell superloading. The gains in throughput associated to increasing cell loading fivefold have far-reaching implications for cell-atlasing experiments.
• The spinDrop method offers extensive benchmarking against other methods, which is not the case with other proof-of-concept microfluidic methods.
As for the lack of general applicability, we would politely like to refute this claim. Higher sensitivity, lower sequencing costs, lowered biases, cell-type specific sorting, competence in processing all input cell types and multi-step processing have applicability across virtually any high-throughput single-cell application, therefore our vision for where the field is heading does not align with these conservative statements.
We have amended the manuscript according to this review using red lettering.
Major concerns: 1. The microfluidic schema proposed here is not conceptually new; see refs.

37, 58, https://doi.org/10.3390/bioengineering9110674
We agree that some sorting and pico-injector microfluidics architecture have been previously described. However we do not think that increased performance i.e. to boost sensitivity and reduce bias and sequencing costs, have been demonstrated before in the manner we describe in the previous bullet-points. We offer the following descriptions to clearly define the differences between spinDrop and the publications mentioned by the reviewer.

R41
Reference 37 5: • VASA-seq: this methodology does not contain a sorter element, the core element of the spinDrop method, and therefore does not correct for the aforementioned biases and also does not provide for the massive reduction in sequencing costs that is one of the key deliverables of our present manuscript. The performance of the sorter in these scenarios is thus questionable.

R42
• In addition, no improvements were made to increase the sensitivity of RNA capture, and therefore there is no real improvement from the Drop-seq reaction conditions, which displays the worst performance (with inDrop) across benchmarking experiments 21,22 .
• This method was only demonstrated for live cell sorting, which limits applicability to the cell-types we demonstrated in addition (fixed and nuclei).
Furthermore, biases in the datasets and the potential for alleviating them were not explored nor quantified.
• Finally, there are many more distinct features in our method, for example the capabilities of sequencing nascent RNA or to process damaged samples or samples with synthetic RNA spike-ins. We believe our approach significantly improves on this works and provides more evidence of performance across modalities using reference datasets for benchmarking. Unfortunately, the work by Clarke et al. does not show any type of benchmarking, and it is therefore complicated to put it in context with other methods.
Last reference by Liu et al. 29: We have carefully reviewed the article forwarded. However we found many caveats to the study, which we delineate in the following bullet points: • The number of beads per cell varies widely for each droplet (multiple beads per cell) while they should be one or less. This would be problematic for downstream analysis as RNA molecules from single-cells will be split between barcodes (see Figure R3.1).
•  Figure R3.1c, which is in addition contaminated by doublebead events as previously mentioned.
• In the Barnyard plot (Figure 4c), the dataset is truncated for human gene counts, which may skew cross-contamination calculations. A histogram plot of (human / (human + mouse) gene counts to observe the proportions of species-specific barcodes without thresholding should have been more informative for ultimately calculating multiplet rates.
• For comparative benchmarking experiments, no explanation on how downsampling normalisation between datasets was performed (and at which depth: reads per cell) was utilised for comparative analysis. Hence the analysis may be skewed by high sequencing coverage in their paper.
• In Figures 4 and 5, gene expression marker plots for the main cell types were absent so it is impossible to confidently claim that the population sorted is effectively T-cells. Again, to validate the sorter, the authors should have also provided sequencing results for the negative (unsorted) channel to demonstrate the sorter is in fact functional.
• no comments on (and indeed no analysis of) improvements in data quality and noise reduction are made, which we extensively discuss and demonstrateand only these results would demonstrate the utility of a novel set-up.
• The analysis focuses on T-cell sorting and therefore does not demonstrate performance on broader atlases or cell-types. R44 • no demonstration of lower sequencing cost, lower bias, high performance for nuclei and fixed cells, cell-type specific sorting, molecular RNA spike-ins, comprehensive benchmarking against other methods.

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We believe there have been plenty of complex multi-step microfluidic platforms that have been published in which increased performance is demonstrated when compared to simpler passive microfluidic systems. We believe the complexity of multistep processing through multiple microfluidic steps to be necessary and constitutes the future of droplet single-cell technologies. As the number of modalities per single-cell increase (ATAC, RNA, Cut&Tag, proteomics etc.), the field will turn towards microfluidic systems like the ones described in this study to achieve complex successive handling steps on single-cell lysates (like the 5EU-seq read-out we propose). We agree with the reviewer that the complexity of implementation remains a barrier and have therefore significantly expanded the Methods section which should allow for implementation in less specialised laboratories.
We would like to also mention that commercial implementations of the sorter (e.g. STYX system from Atrandi Biosciences, https://atrandi.com/styx) and picoinjector or droplet fusion (Tapestri platform from MissionBio https://missionbio.com/products/platform/ or ONYX system from Atrandi Biosciences, https://atrandi.com/onyx) exist, although they have not been implemented for our application (removing biases, increasing sensitivity and implementing multi-step protocols). Therefore, less specialised personnel may opt to work with these companies in the future, if they fail to implement our workflow.
The electrodes used in this study are a simple channel filled with a salt solution (introduced by Sciambi and Abate in 2014 30 ) and in our experience does not change experimental complexity or fabrication efficiency. Perhaps the reviewer refers to the molten alloy electrodes, which need alignment and make the assembly more complex, however this method is outdated.

R47
In the discussion we also added a description on how the sorting throughput can be increased in future iterations of the system: "Sorting throughputs might be increased by using smaller beads that would provide better droplet monodispersity by reducing the droplet volume, or using serial electrodes that can improve sorting speed 67 ".
We have also added a point of discussion regarding cell numbers: "The number of cells analyzed in this study (9,599) with spinDrop is in line with previous droplet proof-of-concepts (10,000 cells for inDrop 14  Although picoinjection adds a microfluidic processing step, this step is performed on cell-containing sorted droplets (which can, in our experience, can be ran at 30-70 Hz), and is therefore overall much faster than the droplet generation/sorting microfluidic step, which has similar throughputs to other single-cell methods (75Hz, but on all droplets, including empty droplets). Therefore, the time required for picoinjection is negligible when put in the broader context of sample preparation, encapsulation, library preparation and sequencing; which can amount to several days of work/processing to obtain sample results. In addition, because our method does not require sample pre-enrichment (either through FACS or MACS), sample preparation times and batch effects due to lengthy sample processing procedures before lysis are greatly reduced using our method compared to other state-of-the-art high-throughput droplet methodologies.
We further agree that the goal of single-cell experiments is to cover cell-types of interest at high-enough coverage to derive statistical power to downstream analyses.  In any case, some experiments should be performed in replicates to demonstrate reproducibility.
We agree with the reviewer that measures of reproducibility would bolster our claims towards the broad applicability of the method. We therefore compared two separate HEK293T replicate samples processed with the spinDrop method. We first downsampled replicate 1 to match the sample size of replicate 2 (n= 610 cells) and compared the number of genes and UMIs per cell, which were broadly equivalent (replicate 1 median gene count per cell= 3,437.5, median UMI count per cell=5,786.5; replicate 2 median gene count per cell= 3,225, median UMI count per cell=5,430) ( Figure R3.2a-b). We then processed the two replicate samples for dimensional reduction and projected them on the two first principal components without any batch effect correction. The two replicates spread homogeneously across both components, with no replicate-specific bias. We then proceeded to compare the average expression for both replicates, which had a R 2 of 0.98, further bolstering reproducibility claims. We have included these new materials in Figure 2C-D and Extended Data Figure 3F-G and added the following statements to the text: "To test the reproducibility of spinDrop, two independent libraries prepared using HEK293T cells as an input were sequenced and analysed, showing similar gene and UMI capture per cell (Extended Data Figure 3F-G). The two replicates homogeneously spread across the two first principal components during dimensional reduction with no library-specific bias ( Figure 2C) and correlation analysis of the average expression per gene showed high inter-replicate homology (R 2 =0.98, Figure 2D).". Minor concerns: 1. When testing FADS, calcein-AM is used to get droplets with single, viable cells at a tested rate of (96.1%, n= 51). N=51 appears to be the single image presented in Figure 2B. Multiple images should have been processed for this test, including from different collections through FADs. A single image could be the result of an above average sorting run, is this 96.1% value consistent across multiple FADS run at different times?
We agree with the reviewer that additional sorting pictures could be provided to strengthen the claims of our sorter working robustly across sample input modalities R51 and experiments. Therefore we have added two additional sorting results in the manuscript pertaining to a sort with 5 times the typical cell loading concentration (to illustrate doublet removal, λ = 0.5) and a lymphoid cell types sorting using PElabelled antibodies. From these additional data, we estimate the sorter to have reliably sorted 92.3% (n=52 droplets) and 95.3% (n=43 droplets) (Figure 3.3), which is in line with the claims from the manuscript. We have appended these new data to the manuscript in Extended Data Figure 2C.

2.
It is unclear why a species-mixing experiment was used to calculate the proportion of reads mapped to a cell barcode. What is the proportion of mapped reads in other spinDrop experiments, e.g., on embryonic mouse brain at E10.5 (Fig 4) and 5EU-seq (fig. 5)?
We apologise if this is unclear in the manuscript. Species-mixing experiments are used to validate that a single barcode represents a single-cell. This can be checked using a mixture of cells from two species as an input, and the number of reads mapping to each species after analysis informs on the relative cross-contamination rates. To R52 evaluate purity, we simply calculate the proportion, for each barcode, of reads mapping to the human over the mouse + human genome. In an ideal scenario, an accurate single-cell method will have, for each barcode, either a proportion of 100% (human) or 0% (mouse). This assay is a gold-standard assay to validate single-cell methodologies. This is quite different from mapping rates to the genome, which inform on the proportion of reads that are usable in downstream analysis postmapping (i.e. to build the count matrix). We have calculated the number of mapped reads for the embryonic mouse brain at E10.5 (72.2%) and 5EU-seq (60.9% for the non-nascent part and 63.4% for the nascent RNA) and inserted these metrics in the Methods section. We apologise for the lack of clarity in the sentence. We detected 690 genes that were robustly differentially expressed during our comparison between 10x and spinDrop. Of this total number of 690 genes, 291 genes were overexpressed in spinDrop, 399 genes were overexpressed in the 10x dataset. We have modified the text accordingly, for clarity:

It is unclear why single cells vs. single nuclei species mixing experiments
"The analysis revealed a total of 690 genes were significantly and robustly differentially expressed throughout the dataset (absolute values for log2 fold change > 1 and Bonferroni adjusted p-values <10 -5 ). Further annotation of the genes by R53 biotypes showed an enrichment for non-coding RNAs and pseudogenes for spinDrop and some protein-coding genes in the 10x Chromium dataset (Extended Data Figure 3D, Supplementary